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Baker CR, Pease M, Sexton DP, Abumoussa A, Chambless LB. Artificial intelligence innovations in neurosurgical oncology: a narrative review. J Neurooncol 2024; 169:489-496. [PMID: 38958849 PMCID: PMC11341589 DOI: 10.1007/s11060-024-04757-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes. METHODS A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors. RESULTS Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens. CONCLUSION While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.
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Affiliation(s)
- Clayton R Baker
- Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Matthew Pease
- Department of Neurosurgery, Indiana University, Indianapolis, IN, USA
| | - Daniel P Sexton
- Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Andrew Abumoussa
- Department of Neurosurgery, University of North Carolina at Chapel Hill Hospitals, Chapel Hill, NC, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
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Wang P, Zhang Z, Xie Z, Liu L, Ren G, Guo Z, Xu L, Yin X, Hu Y, Wang Y, Wu X. Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation. World Neurosurg 2024; 189:e300-e309. [PMID: 38878892 DOI: 10.1016/j.wneu.2024.06.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Accepted: 06/09/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVE To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs). METHODS EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision. RESULTS The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485). CONCLUSIONS This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.
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Affiliation(s)
- PeiYang Wang
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Zhe Zhang
- Department of Orthopaedics, Yancheng Third People's Hospital, Yancheng, Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Lei Liu
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - GuanRui Ren
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - ZongJie Guo
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Li Xu
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - XiangJie Yin
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - YiLi Hu
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - XiaoTao Wu
- Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
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Le KDR, Tay SBP, Choy KT, Verjans J, Sasanelli N, Kong JCH. Applications of natural language processing tools in the surgical journey. Front Surg 2024; 11:1403540. [PMID: 38826809 PMCID: PMC11140056 DOI: 10.3389/fsurg.2024.1403540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024] Open
Abstract
Background Natural language processing tools are becoming increasingly adopted in multiple industries worldwide. They have shown promising results however their use in the field of surgery is under-recognised. Many trials have assessed these benefits in small settings with promising results before large scale adoption can be considered in surgery. This study aims to review the current research and insights into the potential for implementation of natural language processing tools into surgery. Methods A narrative review was conducted following a computer-assisted literature search on Medline, EMBASE and Google Scholar databases. Papers related to natural language processing tools and consideration into their use for surgery were considered. Results Current applications of natural language processing tools within surgery are limited. From the literature, there is evidence of potential improvement in surgical capability and service delivery, such as through the use of these technologies to streamline processes including surgical triaging, data collection and auditing, surgical communication and documentation. Additionally, there is potential to extend these capabilities to surgical academia to improve processes in surgical research and allow innovation in the development of educational resources. Despite these outcomes, the evidence to support these findings are challenged by small sample sizes with limited applicability to broader settings. Conclusion With the increasing adoption of natural language processing technology, such as in popular forms like ChatGPT, there has been increasing research in the use of these tools within surgery to improve surgical workflow and efficiency. This review highlights multifaceted applications of natural language processing within surgery, albeit with clear limitations due to the infancy of the infrastructure available to leverage these technologies. There remains room for more rigorous research into broader capability of natural language processing technology within the field of surgery and the need for cross-sectoral collaboration to understand the ways in which these algorithms can best be integrated.
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Affiliation(s)
- Khang Duy Ricky Le
- Department of General Surgical Specialties, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Geelong Clinical School, Deakin University, Geelong, VIC, Australia
- Department of Medical Education, The University of Melbourne, Melbourne, VIC, Australia
| | - Samuel Boon Ping Tay
- Department of Anaesthesia and Pain Medicine, Eastern Health, Box Hill, VIC, Australia
| | - Kay Tai Choy
- Department of Surgery, Austin Health, Melbourne, VIC, Australia
| | - Johan Verjans
- Australian Institute for Machine Learning (AIML), University of Adelaide, Adelaide, SA, Australia
- Lifelong Health Theme (Platform AI), South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Nicola Sasanelli
- Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, SA, Australia
- Department of Operations (Strategic and International Partnerships), SmartSAT Cooperative Research Centre, Adelaide, SA, Australia
- Agora High Tech, Adelaide, SA, Australia
| | - Joseph C. H. Kong
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Monash University Department of Surgery, Alfred Hospital, Melbourne, VIC, Australia
- Department of Colorectal Surgery, Alfred Hospital, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
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Ramon-Gonen R, Dori A, Shelly S. Towards a practical use of text mining approaches in electrodiagnostic data. Sci Rep 2023; 13:19483. [PMID: 37945618 PMCID: PMC10636146 DOI: 10.1038/s41598-023-45758-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
Healthcare professionals produce abounding textual data in their daily clinical practice. Text mining can yield valuable insights from unstructured data. Extracting insights from multiple information sources is a major challenge in computational medicine. In this study, our objective was to illustrate how combining text mining techniques with statistical methodologies can yield new insights and contribute to the development of neurological and neuromuscular-related health information. We demonstrate how to utilize and derive knowledge from medical text, identify patient groups with similar diagnostic attributes, and examine differences between groups using demographical data and past medical history (PMH). We conducted a retrospective study for all patients who underwent electrodiagnostic (EDX) evaluation in Israel's Sheba Medical Center between May 2016 and February 2022. The data extracted for each patient included demographic data, test results, and unstructured summary reports. We conducted several analyses, including topic modeling that targeted clinical impressions and topic analysis to reveal age- and sex-related differences. The use of suspected clinical condition text enriched the data and generated additional attributes used to find associations between patients' PMH and the emerging diagnosis topics. We identified 6096 abnormal EMG results, of which 58% (n = 3512) were males. Based on the latent Dirichlet allocation algorithm we identified 25 topics that represent different diagnoses. Sex-related differences emerged in 7 topics, 3 male-associated and 4 female-associated. Brachial plexopathy, myasthenia gravis, and NMJ Disorders showed statistically significant age and sex differences. We extracted keywords related to past medical history (n = 37) and tested them for association with the different topics. Several topics revealed a close association with past medical history, for example, length-dependent symmetric axonal polyneuropathy with diabetes mellitus (DM), length-dependent sensory polyneuropathy with chemotherapy treatments and DM, brachial plexopathy with motor vehicle accidents, myasthenia gravis and NMJ disorders with botulin treatments, and amyotrophic lateral sclerosis with swallowing difficulty. Summarizing visualizations were created to easily grasp the results and facilitate focusing on the main insights. In this study, we demonstrate the efficacy of utilizing advanced computational methods in a corpus of textual data to accelerate clinical research. Additionally, using these methods allows for generating clinical insights, which may aid in the development of a decision-making process in real-life clinical practice.
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Affiliation(s)
- Roni Ramon-Gonen
- The Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel.
| | - Amir Dori
- Department of Neurology, Sheba Medical Center, Tel HaShomer, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shahar Shelly
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
- Neuroimmunology Laboratory, The Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Maniar KM, Lassarén P, Rana A, Yao Y, Tewarie IA, Gerstl JVE, Recio Blanco CM, Power LH, Mammi M, Mattie H, Smith TR, Mekary RA. Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis. World Neurosurg 2023; 179:e119-e134. [PMID: 37574189 DOI: 10.1016/j.wneu.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/06/2023] [Indexed: 08/15/2023]
Abstract
BACKGROUND Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction. The aim of this study was to evaluate the performance of published traditional ML versus DL algorithms in classification, grading, outcome prediction, and segmentation of meningiomas. METHODS A systematic review and meta-analysis were conducted. Major databases were searched through September 2021 for publications evaluating traditional ML versus DL models on meningioma management. Performance measures including pooled sensitivity, specificity, F1-score, area under the receiver-operating characteristic curve, positive and negative likelihood ratios (LR+, LR-) along with their respective 95% confidence intervals (95% CIs) were derived using random-effects models. RESULTS Five hundred thirty-four records were screened, and 43 articles were included, regarding classification (3 articles), grading (29), outcome prediction (7), and segmentation (6) of meningiomas. Of the 29 studies that reported on grading, 10 could be meta-analyzed with 2 DL models (sensitivity 0.89, 95% CI: 0.74-0.96; specificity 0.91, 95% CI: 0.45-0.99; LR+ 10.1, 95% CI: 1.33-137; LR- 0.12, 95% CI: 0.04-0.59) and 8 traditional ML (sensitivity 0.74, 95% CI: 0.62-0.83; specificity 0.93, 95% CI: 0.79-0.98; LR+ 10.5, 95% CI: 2.91-39.5; and LR- 0.28, 95% CI: 0.17-0.49). The insufficient performance metrics reported precluded further statistical analysis of other performance metrics. CONCLUSIONS ML on meningiomas is mostly carried out with traditional methods. For meningioma grading, traditional ML methods generally had a higher LR+, while DL models a lower LR-.
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Affiliation(s)
- Krish M Maniar
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Philipp Lassarén
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Aakanksha Rana
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, Massachusetts, United States
| | - Yuxin Yao
- Department of Pharmaceutical Business and Administrative Sciences, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, Massachusetts, United States
| | - Ishaan A Tewarie
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Neurosurgery, Haaglanden Medical Center, The Hague, The Netherlands; Faculty of Medicine, Erasmus University Rotterdam/Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jakob V E Gerstl
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Camila M Recio Blanco
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Northeast National University, Corrientes, Argentina; Prisma Salud, Puerto San Julian, Santa Cruz, Argentina
| | - Liam H Power
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; School of Medicine, Tufts University, Boston, Massachusetts, United States
| | - Marco Mammi
- Neurosurgery Unit, S. Croce e Carle Hospital, Cuneo, Italy
| | - Heather Mattie
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, United States
| | - Rania A Mekary
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Pharmaceutical Business and Administrative Sciences, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, Massachusetts, United States.
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6
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Jimenez AE, Mukherjee D. High-Value Care Outcomes of Meningiomas. Neurosurg Clin N Am 2023; 34:493-504. [DOI: 10.1016/j.nec.2023.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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Khanna O, Fathi Kazerooni A, Farrell CJ, Baldassari MP, Alexander TD, Karsy M, Greenberger BA, Garcia JA, Sako C, Evans JJ, Judy KD, Andrews DW, Flanders AE, Sharan AD, Dicker AP, Shi W, Davatzikos C. Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas. Neurosurgery 2021; 89:928-936. [PMID: 34460921 DOI: 10.1093/neuros/nyab307] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Although World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.
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Affiliation(s)
- Omaditya Khanna
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christopher J Farrell
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael P Baldassari
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Tyler D Alexander
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael Karsy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Benjamin A Greenberger
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jose A Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James J Evans
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Kevin D Judy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - David W Andrews
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam E Flanders
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Ashwini D Sharan
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Zaki MM. Commentary: Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model. Neurosurgery 2021; 88:E321-E322. [PMID: 33476392 DOI: 10.1093/neuros/nyaa591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 10/25/2020] [Indexed: 11/12/2022] Open
Affiliation(s)
- Mark M Zaki
- Harvard Medical School, Boston, Massachusetts.,Harvard Business School, Boston, Massachusetts
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